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JNTUA College Of Engineering (Autonomous),Ananthapuramu
Department of Computer Science & Engineering
PROBABILITY AND STATISTICAL METHODS
Course Code: Semester IV(R20) L T P C : 3 0 0 3

Course Objectives:
• To familiarize the students with the foundations of probability and statistical methods
• To impart probability concepts and statistical methods in various engineering applications
Course Outcomes:
CO1: make use of the concepts of probability and their applications (L3)
CO2: apply discrete and continuous probability distributions (L3)
CO3: classify the concepts of data science and its importance (L4)
CO4: interpret the association of characteristics and through correlation and regression tools (L4)
CO5: Design the components of a classical hypothesis test (L6)
CO6: infer the statistical inferential methods based on small and large sampling tests (L6)

UNIT – I: Descriptive statistics and methods for data science
Data science, Statistics Introduction, Population vs Sample, Collection of data, primary and secondary
data, Type of variable: dependent and independent Categorical and Continuous variables, Data
visualization, Measures of Central tendency, Measures of Variability (spread or variance) Skewness,
Kurtosis, correlation, correlation coefficient, rank correlation, regression coefficients, method of least
squares, regression lines.

UNIT – II: Probability
Probability, probability axioms, addition law and multiplicative law of probability, conditional
probability, Baye’s theorem, random variables (discrete and continuous), probability density functions,
properties, mathematical expectation.

UNIT – III: Probability distributions
Probability distributions: Binomial, Poisson and Normal-their properties (Chebyshevs
inequality).Approximation of the binomial distribution to normal distribution.

UNIT – IV: Estimation and Testing of hypothesis, large sample tests
Estimation-parameters, statistics, sampling distribution, point estimation, Formulation of null hypothesis,
alternative hypothesis, the critical and acceptance regions, level of significance, two types of errors and
power of the test. Large Sample Tests: Test for single proportion, difference of proportions, test for single
mean and difference of means. Confidence interval for parameters in one sample and two sample
problems

UNIT – V: Small sample tests
Student t-distribution (test for single mean, two means and paired t-test), testing of equality of variances
(F-test), χ2 - test for goodness of fit, χ2 - test for independence of attributes.

Textbooks:
1. Miller and Freunds, Probability and Statistics for Engineers,7/e, Pearson, 2008.

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